Information obtained from landform classification is fundamental for understanding physical, chemical, and biological soil processes. Digital elevation models (DEMs) can be used for landform classification using a geomorphic pattern recognition and classification approach such as geomorphons. In this study, we used geomorphons to predict soil types of the Uasin Gishu Plateau in western Kenya with the aim of improving the existing soil type maps. We ran the geomorphons classification on the 30 m Shuttle Radar Topographic Mission (SRTM) DEM using the module r.geomorphons “add-on” in GRASS GIS with look up distance (L) values of 10, 20, 30, 40, 50, 60, 70, 80, 90 and 100 cells (300 m to 3,000 m) and flatness threshold values of 2, 0.5 and 0.01 degrees. We grouped the resulting geomorphons into three classes: upland summits, upland midslopes, and bottomlands. We then assigned soil types according to the World Reference Base soil classification system to these three landscape positions after dividing the study area into the Lower Plateau and the Upper Plateau based on elevation and geomorphology. The predicted soil types were quantitatively evaluated against 50 soil point observations based on overall accuracy, precision, recall and Cohen’s kappa coefficient (k) metrics. Evaluation results showed that an L value of 20 cells performed best (k = 0.52; overall accuracy = 0.62) followed by an L value of 30 cells (k = 0.50; overall accuracy = 0.58). L values of 60, 70 and 80 cells performed worse (k = 0.35; overall accuracy = 0.48). Although an L value of 20 cells performed slightly better than an L value of 30 cells, the L value of 30 cells better captured the geomorphology and soil-landscape relationships based on our own expert knowledge, legacy data, and the fact that the bottomlands pattern was more continuous for an L value of 30 cells than for an L value of 20 cells. Upland summits occurred over ~ 32% of the plateau and were occupied by Nitisols on the Upper Plateau and Ferralsols on the Lower Plateau. Upland midslopes occurred over ~ 42% of the plateau and were occupied by Acrisols on the Upper Plateau and Acrisols/Ferralsols on the Lower Plateau. Bottomlands occurred over ~ 26% of the plateau and were occupied by Luvisols/Gleysols on both the Upper and Lower Plateau. Geomorphons, as a method of landform classification, correlated to geomorpho-pedological processes and captured soil variations and differences in the study area. The approach is computationally efficient and can be used for large areas, but is limited in that it only classifies landscapes according to shape. This means that the algorithm does not separate or identify soil types with different parent materials occurring within one landform class. Thus, Regosols and Cambisols on very steep slopes and Acrisols and Ferralsols on gentle slopes could not be separated.

从地貌分类中获得的信息对于理解物理，化学和生物土壤过程至关重要。数字高程模型（DEM）可用于使用地貌模式识别和分类方法（例如地貌）的地貌分类。在这项研究中，我们使用地貌学来预测肯尼亚西部Uasin Gishu高原的土壤类型，以期改善现有的土壤类型图。我们使用GRASS GIS中的r.geomorphons模块“附加”，在30 m航天飞机雷达地形任务（SRTM）DEM上对地貌进行了分类，查找距离为（L）的10、20、30、40、50、60、70、80、90和100个像元（300 m至3,000 m）的值以及2、0.5和0.01度的平坦度阈值。我们将生成的地貌分类为三类：高地山顶，高地中坡和底地。然后，根据海拔和地貌将研究区域分为下高原和上高原，然后根据世界参考基准土壤分类系统，将土壤类型分配给这三个景观位置。根据总体准确性，精确度，召回率和Cohen卡伯系数（k）指标，针对50个土壤点观测值对预测的土壤类型进行了定量评估。评价结果表明，L值为20的细胞表现最佳（k = 0.52; 总准确度= 0.62），然后是30个像元的L值（k  = 0.50；总准确度= 0.58）。60、70和80个像元的L值表现较差（k  = 0.35；总准确度= 0.48）。虽然大号20个细胞的值比表现稍好大号30个细胞的值时，大号30个细胞的价值更好地捕获基于我们自己的专业知识，传统数据地貌和土壤景观的关系，而事实上，在低洼模式是用于更连续的大号比对于30个细胞的值大号价值20个单元格。高原顶峰发生在高原的约32％处，并被高原上的尼蒂索尔和高原下的Ferralsols占据。高原中坡发生在高原的约42％处，并被上部高原的Acrisols和下部高原的Acrisols / Ferralsols占据。低谷发生在高原的约26％处，在上高原和下高原都被Luvisols / Gleysols占据。地貌作为一种地貌分类方法，与地貌学过程相关，并捕获了研究区域内的土壤变化和差异。该方法计算效率高，可用于大面积区域，但局限性在于它仅根据形状对景观进行分类。这意味着该算法不会使用一种地形类别中出现的具有不同母体材料的土壤类型进行分离或识别。因此，无法分离非常陡峭的斜坡上的雷高溶胶和坎比索尔，以及平缓的斜坡上的阿克里索斯和铁铝溶胶。